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Extract Mask from Neuroimaging Object

Source: R/all_generic.R, R/clustervol.R, R/filebacked_neurovec.R, and 6 more
mask-methods.Rd

Generic function to extract or generate a mask from neuroimaging objects. For sparse objects with a @mask slot, returns the stored mask. For dense objects, returns a filled mask (all TRUE values) indicating all voxels contain valid data.

Usage

mask(x)

# S4 method for class 'ClusteredNeuroVol'
mask(x)

# S4 method for class 'FileBackedNeuroVec'
mask(x)

# S4 method for class 'MappedNeuroVec'
mask(x)

# S4 method for class 'NeuroHyperVec'
mask(x)

# S4 method for class 'NeuroSlice'
mask(x)

# S4 method for class 'DenseNeuroVec'
mask(x)

# S4 method for class 'DenseNeuroVol'
mask(x)

# S4 method for class 'LogicalNeuroVol'
mask(x)

# S4 method for class 'AbstractSparseNeuroVec'
mask(x)

# S4 method for class 'SparseNeuroVecSource'
mask(x)

Arguments

x

A neuroimaging object (NeuroVol, NeuroVec, or derived classes)

Value

A LogicalNeuroVol object representing the mask

Details

The behavior depends on the class of the input object:

  • For sparse objects (SparseNeuroVec, ClusteredNeuroVol, etc.): Returns the stored @mask slot

  • For dense objects (DenseNeuroVol, DenseNeuroVec, etc.): Returns a LogicalNeuroVol with all TRUE values

  • For ROI objects: Not implemented (use coords() instead)

Examples

# Create a dense volume
vol <- NeuroVol(array(rnorm(64^3), c(64,64,64)), NeuroSpace(c(64,64,64)))
m <- mask(vol)  # Returns all TRUE mask

# Create a sparse vector with explicit mask
mask_array <- array(runif(64^3) > 0.5, c(64,64,64))
mask_vol <- LogicalNeuroVol(mask_array, NeuroSpace(c(64,64,64)))
# Data must be a matrix (time x masked voxels)
sparse_data <- matrix(rnorm(sum(mask_array) * 10), nrow = 10, ncol = sum(mask_array))
svec <- SparseNeuroVec(sparse_data, NeuroSpace(c(64,64,64,10)), mask_vol)
m2 <- mask(svec)  # Returns the stored mask